Machine Learning Software Engineering Intern

At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.

Cadence has a 30+ year history of applying leading edge optimization and analysis algorithms to extremely complex problems in semiconductor and electronic design, verification, and analysis. The Cadence Virtuoso group is exploring the application of Deep and Machine Learning techniques to Electronic Design Automation [EDA] tools, as well as the application of EDA/Computer-Aided Design algorithms to Deep Learning software.

You will collaborate with our team to investigate techniques and tools, develop working prototypes on projects that are pre-defined or ones which you will help define. The goal is to tackle specific sub-problems in placement and routing which lead to better final layout quality. Join us to drive research in this exciting area.

Investigate and experiment different ML model algorithms and apply reinforcement learning training methods that can be incorporated in the custom analog cell and device level place and route flow. This will involve learning the current automatic place and route flow, using ML framework available on Virtuoso and developing models that can be used in advanced node product flows. The developed models will generate a partial layout which can work with established place-and-route algorithms with the goal of improving the final quality of layout and improve performance.

Requirements:

• A full-time student pursuing a MS/PhD in some combination of machine learning, data science, computer science, or engineering

• Coding in C++/Python, strong knowledge of ML algorithms and development frameworks like Tensor Flow, PyTorch etc.

• Familiarity with fundamental machine learning techniques such as classification, regression, clustering and Reinforcement Learning

Good to have: Knowledge of circuits and analog design is a plus.

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